Data Warehouse How Businesses use Data Warehousing

Introduction

Data warehousing is a technological way for businesses to align data with performance benchmarks so that organizations can obtain a long-range view of aggregated data and engage in complex analytics. These analytics typically give the organization a better understanding of what its stockpile of information means, what data trends over time reveal, and what the data indicates is in store for the business in the future. This paper will provide a description of data warehousing, examples of how it is used in a business, challenges that an organization might face when utilizing a data warehouse (i.e., how it can be implemented and what type of training is required to run it), how data warehousing may change in the next five years, and what organizational leaders can do to be prepared.

What is a Data Warehouse?

A data warehouse is a digital storage facility that integrates data from numerous sources within a business. As most businesses have multiple divisions and departments, each of these can act as a data source or stream that flows into the organization’s data warehouse. A firm’s sales department, finance department, marketing department and so on would each send their data to the data warehouse. Once there, the data can then be accessed and analyzed by stakeholders in the firm, who require analytical reports for planning or evaluation purposes. The data warehouse can be used to store information for e-mails, a company web server, shipping information, sales info, marketing data, financial systems, supply chain information, customer data, transactions, payrolls and more (Bhat & Bose, 2018).

The data warehouse also serves as a backup for data from the source system that provides it—which means that if the source system is ever corrupted or compromised, data of that system is not necessarily lost, as it can still be retrieved from the data warehouse. The data warehouse can be arranged in diverse ways, depending on the type of architecture used to set it up; it offers the possibility for data integration, a variety of tool and software applications for different users’ needs; and the processing of Big Data ore metadata on a routine basis (Rainer & Cegielski, 2012).

Examples of How a Data Warehouse is Applied in Business

There are a variety of designs that can be used when applying the data warehouse in a business setting. The bottom-up design is the most basic example: it allows a business to produce reports and analyses that can be created in data marts, which...

The data marts communicate with one another using a specific mode of information sharing that they each share in common.
Then there is the top-down example, which is the inverse of the bottom-up: in this example, the data warehouse is conceived with the most minute data terms possible stored within it. When a business requires a specific analysis, the data marts are established within the data warehouse, whereas in the bottom-up approach, the data marts are created first based on specific business functions that are required.

In practical terms, the data warehouse could be used by a business to track customers or to track employees. For instance, if a business wants to track what its clients and consumers are doing in terms of products browsed, products purchased, promotions utilized, and so on, it can track all of this data by incorporating customer data from any data source that the business operates—whether that is the cash register (the point of sale), the company’s website, the company’s call center, the company’s mailing list, and so on. A business can collect, process and analyze information about how a consumer shops online, what the consumer looks at, how many minutes the consumer spends on any one webpage, where the consumer goes from there, where the consumer comes from to get to the page, etc. (Debortoli, Müller & vom Brocke, 2014).

This is what online companies like Amazon or Best Buy do; it is what Google does with its analytics; it is what Facebook and other social media sites do (and it is actually part of their business model: they collect this data in their data warehouse which they then use to show advertisers that they can target specific individuals with tailor-made ads, so to speak). Mesa, for example, is a type of data warehouse used for the advertising system run by Google (Gupta et al., 2016). For Google, Mesa “ingests data generated by upstream services, aggregates and persists the data internally, and serves the data via user queries” (Gupta et al., 2016, p. 117). Mesa is integrated with other data warehouses used by Google, and thus is able to leverage the data services of Google’s Colossus and MapReduce as well (Gupta et al., 2016). The more data the business has, the more interlocking systems can become and more leverage over data analytics a company can maintain.

Challenges

One of the biggest challenges related to data warehousing is the challenge “to consolidate data to create a single point of truth for all…